The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning. Learn more →
C++ Gbdt Projects
-
xgboost
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
-
LightGBM
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
-
WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
Project mention: SIRUS.jl: Interpretable Machine Learning via Rule Extraction | /r/Julia | 2023-06-29SIRUS.jl is a pure Julia implementation of the SIRUS algorithm by Bénard et al. (2021). The algorithm is a rule-based machine learning model meaning that it is fully interpretable. The algorithm does this by firstly fitting a random forests and then converting this forest to rules. Furthermore, the algorithm is stable and achieves a predictive performance that is comparable to LightGBM, a state-of-the-art gradient boosting model created by Microsoft. Interpretability, stability, and predictive performance are described in more detail below.
C++ Gbdt related posts
- XGBoost 2.0
- XGBoost2.0
- SIRUS.jl: Interpretable Machine Learning via Rule Extraction
- [D] RAM speeds for tabular machine learning algorithms
- Xgboost: Banding continuous variables vs keeping raw data
- [P] LightGBM but lighter in another language?
- XGBoost Save and Load Error
-
A note from our sponsor - WorkOS
workos.com | 24 Apr 2024
Index
Sponsored